Every time a large model undergoes a capability iteration, there is a wave of "industrial software termination theory" in the industry. On one side, AI entrepreneurs are shouting 'Refactoring industrial software with big models', as if a set of big models can penetrate the moat that Dassault, Siemens, and ANSYS have accumulated for half a century; On the other hand, traditional industrial software manufacturers are busy adding AI chat boxes to their products, packaging "AI assisted drawing" and "AI automatic grid drawing" as "AI native", insisting that AI is just a plugin to improve efficiency and cannot touch the foundation of industrial software.

Behind the two extreme voices lies the same cognitive misconception: everyone is talking about "what AI can bring to industrial software", but no one returns to the fundamental question - why was industrial software born? Why do we really need it?

Without understanding this' meta problem ', all discussions about' subversion 'and' moat 'are groundless. To answer whether industrial software will be killed by AI, we must start from the essence of industrial software and gradually deduce the ultimate outcome.

1The Birth of Industrial Software: Why Do We Need Industrial Software?

The inherent nature of industrial software has never been "software", but a convergence tool for industrial system uncertainty.The ultimate demand of industrial production has always been one:Using the lowest cost, shortest cycle, highest quality, and minimum risk, complete the full process transformation of industrial products from "human demand" to "physical implementation", transforming the non-standard, random, error prone, and uncontrollable physical world into standardized, deterministic, reusable, and predictable stable output, and achieving socialized mass production。

The biggest enemy of the industrial system has always been "uncertainty": human error in hand drawn drawings, high cost of physical trial and error, production fluctuations caused by manual control, information gaps in cross job collaboration, and the irreplicability of master experience... These uncertainties are the "entropy increase" of the industrial system and the core bottleneck that restricts the development of industrial scale.

The birth of industrial software is to solve this core problem. Its core mission is to continuously transform the implicit experience, non-standard judgments, and manual operations that can only be mastered by humans in the industrial field into digital rules that can be encoded, solidified, reused, and automatically executed, and to continuously converge the entropy increase of the industrial system.

🔷 The replacement of hand drawn drawings with CAD has converged the errors and non standardization of manual drawing, providing a unified and reusable digital standard for product design;

🔷 CAE simulation replaces physical trial and error, which converges the uncertainty and trial and error costs of product development, allowing physical experiments that originally required several months and tens of millions to be verified in a few days in the digital world;

🔷 MES/DCS replaces manual control of production lines, which converges the fluctuations and uncontrollability of the production process, providing a stable control tool for the quality, efficiency, and cost of large-scale production;

🔷 PLM replaces manual management of documents and BOMs, which converges data chaos and collaborative losses throughout the entire lifecycle, providing a unified digital carrier for cross departmental and cross enterprise collaboration.

This is the essence of industrial software: its core value has never been the code itself, but the industrial mechanisms, process knowledge, industry experience, and process specifications that are embedded in the code. It is the digital carrier of industrial know-how and the underlying tool that sets the rules for the industrial world.

2、 What problems have industrial software solved in the past few decades?

By understanding the essence, we can clearly break down the problems solved by industrial software in the past into two layers:

🔷 The first layer is the problem of the industry itself - converging uncertainty, solidifying industrial rules, reducing trial and error costs, and achieving large-scale production. This is the core of industrial software and the fundamental meaning of its birth.

🔷 The second layer is the problem brought by "people" - because in the past industrial system, people were the only decision-making and execution subject in the entire process, and industrial software had to adapt to people's ability boundaries, division of labor modes, and collaborative logic. This is the form shell of industrial software, an inevitable product of a specific era.

The vast majority of industrial software forms we see today, from complex UI interfaces, fragmented functional modules, to cumbersome approval processes and version management systems, 90% are not designed to solve industrial problems themselves, but to adapt to the core node of "human".

Why do CAD, CAE, CAM, and PLM need to be separated into independent software modules? It's not that industrial processes must be broken down, but rather that people's professions, occupations, and positions are broken down. Designers use CAD, simulation engineers use CAE, process engineers use CAM, project managers use PLM, and software must match the division of labor of people in order to be used.

Why do industrial software require complex operations with hundreds of menus and years of learning to master? It's not that industrial problems must be so complex, but rather that people need to operate, understand, and verify step by step. Software must provide a "ladder" for people to operate, so that they can transform their professional abilities into industrial output through software.

Why does PLM have a complex system for change approval, version control, and division of responsibilities? It is not necessary for industrial processes to have these links, but to solve the "trust, responsibility, and interest disputes between people" - to prevent problems, disputes, information asymmetry, and unclear rights and responsibilities, essentially dealing with the "human world".

Why is it almost impossible for industrial software from different industries to be universally used? The barriers between PLM in the automotive industry and PLM in the aviation industry are distinct, not because there is a fundamental difference in industrial mechanisms, but because people in different industries have different process experience, process specifications, and work habits, and software must adapt to the industry attributes of people.

In summary, over the past few decades, industrial software has been doing two things - one is to "set rules" for the industrial world, and the other is to "build a ladder" for the "people" who operate the rules. The vast majority of industrial software we are debating and seeing today is the 'ladder' rather than the 'rules' themselves.

3、 What have the core issues in the industrial field changed in the era of AI?

AI， Especially the breakthrough in capabilities brought about by large models has precisely broken through the core premise of the industrial system in the past few decades: people are no longer the necessary executing and decision-making subjects for the entire industrial process.

For the first time, the universal big model has enabled digital systems to handle implicit knowledge and complex coupling problems that humans cannot break down, logicalize, or encode. Previously, requirements decomposition, design iteration, simulation verification, process planning, and collaborative docking that had to be completed by humans can now be independently completed by AI; In the past, industrial software had to adapt to human capability boundaries, division of labor patterns, and collaborative logic, but now with people leaving the execution layer, it has completely lost its meaning of existence.

This leads to the sharpest soul questioning: when the analysis, design, testing, delivery, and manufacturing of industrial activities are all carried out by AI, and there are no more people left, whose efficiency can industrial software improve? If it's just about optimizing computing power, algorithms, and models, is it still related to industrial software itself?

To answer this question, we must first recognize that in the era of AI, the ultimate goal of the industrial sector has not changed - it is still to converge uncertainty and achieve low-cost, high-quality, and large-scale industrial production. But the core bottleneck of the industrial system has completely changed.

In the past, the core bottleneck of industrial systems was the boundary of human abilities: long learning cycles, operational errors, limited energy, and loss of collaboration. The core goal of industrial software is to help people improve efficiency and break through human ability boundaries.

In the era of AI, the core bottleneck of industrial systems has become the reliability of AI's industrial implementation: the core logic of general AI is "probability generation", allowing for "approximate correctness" and even illusions; But the core logic in the industrial field is "absolute certainty". A deviation in one parameter can lead to product scrap, production line shutdown, and even major safety accidents. Zero fault tolerance is an unbreakable bottom line.

General AI can generate imaginative designs, but it cannot make autonomous judgments:

👉 Does this design comply with industry regulations?

👉 Can the fatigue strength of the material meet a 10-year service life?

👉 Can the production plan be adapted to the equipment accuracy of the existing production line?

👉 Will there be safety risks under extreme working conditions?

General AI can complete single point reasoning tasks, but it cannot independently complete the entire process loop from requirement decomposition, design iteration, simulation verification, process planning, production scheduling, supply chain collaboration, and operation and maintenance support. It cannot coordinate the coupling and collaboration of multiple links, multiple subjects, and multiple physical fields.

This is a natural gap between AI and industry that cannot be bridged by general computing power, algorithms, or large models. AI has solved the core bottleneck of "human" in the past, but it has also brought new core problems: how to transform AI's general intelligence into industrial productivity that can be implemented, verified, zero fault tolerance, and closed-loop?

And this issue is precisely the core mission of industrial software, and also the fundamental reason why it cannot be replaced in the AI era.

4、 In the era of AI, the definition of industrial software should be rewritten

The fixed definition of industrial software in the industry is essentially a collection of solutions to past industrial problems, based on the premise that "people are the core decision-making nodes of the entire industrial process", divided into four categories: research and development design, production and manufacturing, business management, and operation and maintenance services.

But we must admit that technology has changed, the core issues in the industrial field have changed, and the definition and boundaries of industrial software cannot be solidified.

Based on our deduction of the essence of industrial software, industrial software in the AI era should have a completely new definition:

Industrial software in the AI era is a collection of digital encapsulation and executable capabilities of knowledge, mechanisms, rules, and processes throughout the entire lifecycle of the industrial field. It is the core ontology of industrial AI intelligent agents; It endows AI with the ability to solve problems throughout the entire process of requirement decomposition, design and development, production and manufacturing, delivery and operation in industrial scenarios, which can be implemented, verified, closed-loop, and evolved. It is the core digital infrastructure for the autonomous evolution of industrial systems in the digital age.

This definition completely overturns the rigid understanding of industrial software in the past and clearly responds to the soul questioning:

🔷 Firstly, the service target of industrial software has completely shifted from "people" to "industrial AI agents". It no longer needs to provide a ladder for human operation, nor does it need to adapt to human division of labor, habits, and ability boundaries. It only needs to provide AI with callable, executable, fenced, and traceable industrial capabilities.

🔷 Secondly, the core value of industrial software has shifted from "improving human efficiency" to "defining the industrial capability boundaries of AI". The saying 'if there is no one left, there is no need to improve efficiency' essentially confuses' means' and 'purpose' - improving human efficiency has never been the ultimate goal of industrial software, but rather a means for a specific era. Its ultimate goal is to improve the conversion efficiency of the entire industrial system from "demand" to "physical implementation", and to converge the uncertainty of the entire industrial system. This mission has not disappeared in the era of AI, but has become even more important.

🔷 Thirdly, the core ontology of industrial software has transformed from a "suite of tool software" to an "executable collection of industrial capabilities". It is no longer a dead code written line by line, a broken functional module, but a capability unit that encapsulates industrial know-how, mechanism models, and compliance rules accumulated over hundreds of years into AI that can be directly called. The computing power and algorithms of general AI are only the "body" of industrial AI, while the set of capabilities of industrial software is the "soul" of industrial AI.

In one sentence: AI cannot kill industrial software, it will only kill the redundant forms that exist in industrial software to adapt to humans. The true industrial software core will transform from a 'tool for people' to the 'industrial soul of AI'.

5、 What form will future industrial software exist in?

Since the core ontology of industrial software is a collection of industrial capabilities for AI agents, its ultimate form is not the suite software we see today, nor the technical components such as RAG and MCP that are hotly discussed in the industry - these are just the "steel and cement" that build the ultimate form, rather than the "industrial building" itself.

🔷 RAG (Retrieval Enhanced Generation) is just a knowledge retrieval and verification component for future industrial software. It solves the problems of AI knowledge invocation and illusion suppression, but cannot execute industrial actions, verify industrial mechanisms, or closed-loop industrial processes, and will never become the ontology of industrial software.

🔷 MCP (Model Context Protocol) is just a communication and collaboration protocol for future industrial software. It solves the context synchronization problem between AI, capability modules, and multiple AI. It does not carry any industrial capabilities and does not have an industrial core. MCP is just an empty protocol framework.

🔷 Industrial AI agents are only the executing entities of future industrial software, the "users" of industrial software, rather than the industrial software itself. Just as humans are users of traditional industrial software, rather than the software itself, the industrial capabilities that an agent can unleash depend entirely on the underlying industrial software's ability accumulation.

The ultimate form of future industrial software is the Industrial Agent OS (IAOS), which is an industrial native intelligent agent operating system for industrial AI agents. All the core values of traditional industrial software will ultimately be dismantled, restructured, and encapsulated into the core kernel of this operating system.

This operating system completely strips away all redundant designs that exist for human adaptation, and consists of a 5-layer core architecture, each layer strictly anchoring the essential mission of industrial software:

1. Atomization industrial capability core (the core ontology of future industrial software)

This is the soul of the entire operating system and the ultimate destination of traditional industrial software. It thoroughly disassembles, reconstructs, and encapsulates the core values of traditional industrial software such as CAD/CAE/CAM/EDA/MES/PLM into atomized, composable, executable, fenced, and traceable industrial capability operators.

It is no longer a complete CAD suite, but is broken down into 3D parametric modeling operators, BOM automatic generation operators GD& T compliance verification operator; It is no longer a complete CAE software, but is broken down into structural mechanics simulation operators, fluid field simulation operators, and fatigue life verification operators. Each operator comes with industrial mechanism barriers, compliance boundaries, and error verification logic. When called by AI, it will automatically complete execution, verification, and error correction, fundamentally eliminating industrial illusions.

The core competition of future industrial software manufacturers will never be AI algorithms, but the depth, breadth, and reliability of this operator library - this is an industrial moat that can never be replaced by general AI and has been sedimented for hundreds of years.

2. Industrial level knowledge and data base

This is the "knowledge ammunition library" of the operating system, the supporting support for industrial capability operators, and the core carrier of the industrial enhanced version of RAG. It has constructed an industrial multimodal knowledge graph and a full lifecycle time-series data lake, which includes global industrial mechanism formulas, industry standards, regulatory specifications, process cases, fault databases, and other comprehensive industrial knowledge. It achieves the full chain capability of "retrieval verification association update closed-loop", providing full, accurate, and dynamically updated industrial knowledge support for AI decision-making.

3. Industrial Agent Communication and Collaboration Protocol Layer

This is the "neural network" of the operating system and the core carrier of the industrial enhanced version MCP. It solves three major pain points in industrial collaboration: collaborative calling of different capability operators within a single agent, cross scenario collaboration of multiple industrial agents within the same enterprise, and secure collaboration of upstream and downstream cross subject agents in the industrial chain. At the same time, industrial exclusive capabilities such as division of rights and responsibilities, intellectual property isolation, and compliance auditing have been added, fully adapting to the strong compliance and security requirements of industrial scenarios.

4. Industrial Agent Scheduling and Scheduling Engine

This is the 'brain center' of the operating system, serving as the core bridge connecting the general AI capabilities with the industrial capabilities core. It is based on the top-level industrial goals input by humans, autonomously completing goal decomposition, operator scheduling, process orchestration, multi-agent collaboration, risk prediction, and closed-loop optimization, truly achieving end-to-end autonomous execution from top-level requirements to physical implementation.

5. Minimalist human-computer interaction entrance layer

This is the only part of the entire system that is related to humans, completely stripping away all redundant designs and retaining only two core entrances: the top-level target natural language input port and the final result and extreme abnormal alarm output port. Humans only need to input core industrial goals and constraints, without needing to understand any software operations or industrial details. All intermediate execution processes are completely black boxed, completely returning to the essence of industrial software solving industrial problems.

6、 Final answer: Industrial software will not be killed by AI, it will only be awakened by AI

By this point, the title's question has a clear answer: industrial software will not be killed by AI, on the contrary, AI will enable industrial software to completely break free from the shackles of "tools" and return to its original mission when it was born.

In the past, the capability boundaries of industrial software were locked by human cognitive boundaries, operational capabilities, and collaborative efficiency. It had to spend a lot of energy building ladders and adapting, but instead deviated from its core mission of "setting rules for the industrial world and converging uncertainty".

In the future, AI will completely dismantle the decades old "ladder" and make the core of industrial software the soul of industrial AI. It no longer requires the ability to adapt to humans, but only needs to focus on the rules, mechanisms, knowledge, and laws of industry itself. Its ability boundary will expand from "industrial knowledge known to humans" to "infinite industrial laws that AI can discover".      

It's never AI killing industrial software, but industrial software that will truly enter the deep waters of industry with AI.